class_docstr: Dataset to manage the data loading, augmentation and generation. 
docstr: Parameters
  ----------
  file_list : list
      a list of file path and annotations
  transforms : function
      function for data augmentation
  data_root : str
      the root path of dataset
  img_size : int
      the size of image height or width
docstr_tok: ["Parameters","--","--","--","--","--","file_list",":","list","a","list","of","file","path","and","annotations","transforms",":","function","function","for","data","augmentation","data_root",":","str","the","root","path","of","dataset","img_size",":","int","the","size","of","image","height","or","width"]
code: def __init__(
      self, file_list, transforms=None, data_root="", img_size=112
  ):
      """
      Parameters
      ----------
      file_list : list
          a list of file path and annotations
      transforms : function
          function for data augmentation
      data_root : str
          the root path of dataset
      img_size : int
          the size of image height or width
      """
      self.line = None
      self.path = None
      self.img_size = img_size
      self.land = None
      self.angle = None
      self.data_root = data_root
      self.transforms = transforms
      with open(file_list, "r") as f:
          self.lines = f.readlines()
  
file_path: nni/examples/nas/legacy/oneshot/pfld/datasets.py
imports: from __future__ import absolute_import, division, print_function
  import cv2
  import os
  import numpy
  from torch.utils import data
label_desc: Use image node_id 1 to create a new PFLDDatasets object from inherited base classes: data with example: obj = PFLDDatasets(file_list, transforms, data_root, img_size)